Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
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.