  We study the U.S. Operations Research/Industrial-Systems Engineering (ORIE)
faculty hiring network, consisting of 1,179 faculty origin and destination data
together with attribute data from 83 ORIE departments. A social network
analysis of faculty hires can reveal important patterns in an academic field,
such as the existence of a hierarchy or sociological aspects such as the
presence of communities of departments. We first statistically test for the
existence of a linear hierarchy in the network and for its steepness. We find a
near linear hierarchical order of the departments, proposing a new index for
hiring networks, which we contrast with other indicators of hierarchy,
including published rankings. A single index is not capable to capture the full
structure of a complex network, however, so we next fit a latent exponential
random graph model (ERGM) to the network, which is able to reproduce its main
observed characteristics: high incidence of self-hiring, skewed out-degree
distribution, low density and clustering. Finally, we use the latent variables
in the ERGM to simplify the network to one where faculty hires take place among
three groups of departments. We contrast our findings with those reported for
other related disciplines, Computer Science and Business.
