Detailed decompositions in nonlinear models

Boris Kaiser

Applied Economics Letters, 2015, vol. 22, issue 1, 25-29

Abstract: We propose a new approach for performing detailed decompositions of average outcome differentials when outcome models are nonlinear. The method can be flexibly applied to all generalized linear models, which are widely used in empirical research. The advantage over other approaches in the literature is that the effects of group-specific differences in covariate distributions are taken into account. At the same time, desirable features such as path independence are still satisfied. A simulation exercise demonstrates that our decomposition method produces more convincing results than existing methods.

Date: 2015
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DOI: 10.1080/13504851.2014.907469

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