The distribution of citations received by scientific publications can be
approximated by a power law, a finding that has been explained by
“cumulative advantage”. This paper argues that socially embedded
learning is a plausible mechanism behind this cumulative advantage. A
model assuming that scientists face a time trade-off between learning
and writing papers, that they learn the papers known by their peers, and
that they cite papers they know, generates a power law distribution of
popularity, and a shifted power law for the distribution of citations
received. The two distributions flatten if there is relatively more
learning. The predicted exponent for the distribution of citations is
independent of the average in-(or out-) degree, contrary to an untested
prediction of the reference model (Price, 1976). Using publicly
available citation networks, an estimate of the share of time devoted to
learning (against producing) is given around two thirds.
Keywords: shifted power law, scale free networks, two-mode networks, cumulative advantage, polynomial attachment kernel, innovation, diffusion.
JEL codes: D83, D85, O31, O33