  In this article, we investigate the role of gender in collaboration patterns
by analyzing gender-based homophily -- the tendency for researchers to
co-author with individuals of the same gender. We develop and apply novel
methodology to the corpus of JSTOR articles, a broad scholarly landscape, which
we analyze at various levels of granularity. Most notably, for a precise
analysis of gender homophily, we develop methodology which explicitly accounts
for the fact that the data comprises heterogeneous intellectual communities and
that not all authorships are exchangeable. In particular, we distinguish three
phenomena which may affect the distribution of observed gender homophily in
collaborations: a structural component that is due to demographics and
non-gendered authorship norms of a scholarly community, a compositional
component which is driven by varying gender representation across
sub-disciplines and time, and a behavioral component which we define as the
remainder of observed gender homophily after its structural and compositional
components have been taken into account. Using minimal modeling assumptions,
the methodology we develop allows us to test for behavioral homophily. We find
that statistically significant behavioral homophily can be detected across the
JSTOR corpus and show that this finding is robust to missing gender indicators
in our data. In a secondary analysis, we show that the proportion of women
representation in a field is positively associated with the probability of
finding statistically significant behavioral homophily.
