We study the evolution of social distance among economists over the period 1970–2000. While the number of economists has more than doubled, the distance between them, which was already small, has declined significantly. The key to understanding the short average distances is the observation that economics is spanned by a collection of interlinked stars. A star is an economist who writes with many other economists, most of whom have few coauthors and generally do not write with each other.

We study how knowledge about the social network of an individual researcher, as embodied in his coauthor relations, helps us in developing a more accurate prediction of his or her future productivity. We find that incorporating information about coauthor networks leads to a modest improvement in the accuracy of forecasts on individual output, over and above what we can predict based on the knowledge of past individual output. Second, we find that the informativeness of networks dissipates over the lifetime of a researcher's career. This suggests that the signaling content of the network is quantitatively more important than the flow of ideas.

The inability to see and quantify systemic financial risk comes at an immense social cost. Systemic risk in the financial system arises to a large extent as a consequence of the interconnectedness of its institutions, which are linked through networks of different types of financial contracts, such as credit, derivatives, foreign exchange, and securities. The interplay of the various exposure networks can be represented as layers in a financial multi-layer network. In this work we quantify the daily contributions to systemic risk from four layers of the Mexican banking system from 2007 to 2013. We show that focusing on a single layer underestimates the total systemic risk by up to 90%. By assigning systemic risk levels to individual banks we study the systemic risk profile of the Mexican banking system on all market layers. This profile can be used to quantify systemic risk on a national level in terms of nation-wide expected systemic losses. We show that market-based systemic risk indicators systematically underestimate expected systemic losses. We find that expected systemic losses are up to a factor of four higher now than before the financial crisis of 2007–2008. We find that systemic risk contributions of individual transactions can be up to a factor of one thousand higher than the corresponding credit risk, which creates huge risks for the public. We find an intriguing non-linear effect whereby the sum of systemic risk of all layers underestimates the total risk. The method presented here is the first objective data-driven quantification of systemic risk on national scales that reveal its true levels.