Scientific discovery is shaped by scientists' choices and thus by their career patterns. The increasing knowledge required to work at the frontier of science makes it harder for an individual to embark on unexplored paths. Yet collaborations can reduce learning costs-albeit at the expense of increased coordination costs. In this article, we use data on the publication histories of a very large sample of physicists to measure the effects of knowledge and social relatedness on their diversification strategies. Using bipartite networks, we compute a measure of topic similarity and a measure of social proximity. We find that scientists' strategies are not random, and that they are significantly affected by both. Knowledge relatedness across topics explains ≈ 10% of logistic regression deviances and social relatedness as much as ≈ 30% , suggesting that science is an eminently social enterprise: when scientists move out of their core specialization, they do so through collaborations. Interestingly, we also find a significant negative interaction between knowledge and social relatedness, suggesting that the farther scientists move from their specialization, the more they rely on collaborations. Our results provide a starting point for broader quantitative analyses of scientific diversification strategies, which could also be extended to the domain of technological innovation-offering insights from a comparative and policy perspective.
This paper empirically investigates how the inter-sectoral knowledge flows affect the international competitiveness of industries, once controlling for both cost and other technological factors. Using patent data on 14 manufacturing industries in 16 OECD countries over the period 1995-2009, we apply a network-based approach to capture the effect of industries' position in the flows of technological knowledge across industries, which we label inter-sectoral knowledge space. We find that (i) centrality and local clustering in the inter-sectoral knowledge space positively affect the export market shares of an industry, (ii) such two effects are rather redundant and, (iii) national-level knowledge flows' impacts on international competitiveness are way stronger than international ones. Network measures of position in the knowledge space are found to be more relevant than standard technological indicators such as patent counts. Our results point to the importance of industries being well located in the stream of knowledge flows, rather than being innovative per-se, and offers an novel yet robust proxy to measure technological factors affecting trade performances. In addition, we find evidence of geographical boundaries of knowledge flows.
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