  Economic interactions often occur in networks where heterogeneous agents
(such as workers or firms) sort and produce. However, most existing estimation
approaches either require the network to be dense, which is at odds with many
empirical networks, or they require restricting the form of heterogeneity and
the network formation process. We show how the functional differencing approach
introduced by Bonhomme (2012) in the context of panel data, can be applied in
network settings to derive moment restrictions on model parameters and average
effects. Those restrictions are valid irrespective of the form of
heterogeneity, and they hold in both dense and sparse networks. We illustrate
the analysis with linear and nonlinear models of matched employer-employee
data, in the spirit of the model introduced by Abowd, Kramarz, and Margolis
(1999).
