  Recent advances in the literature of decomposition methods in economics have
allowed for the identification and estimation of detailed wage gap
decompositions. In this context, building reliable counterfactuals requires
using tighter controls to ensure that similar workers are correctly identified
by making sure that important unobserved variables such as skills are
controlled for, as well as comparing only workers with similar observable
characteristics. This paper contributes to the wage decomposition literature in
two main ways: (i) developing an economic principled network based approach to
control for unobserved worker skills heterogeneity in the presence of potential
discrimination; and (ii) extending existing generic decomposition tools to
accommodate for potential lack of overlapping supports in covariates between
groups being compared, which is likely to be the norm in more detailed
decompositions. We illustrate the methodology by decomposing the gender wage
gap in Brazil.
