Many theories of fairness distinguish between compensation factors (‘luck’) and responsibility factors (‘effort’). Whereas the distinction between both types of factors is a matter of definition in theory, empirical work usually requires a sharp cut. All determinants of the outcome of interest have to be classified as either a compensation factor or a responsibility factor. We argue that the determinants are often hard to classify. A pragmatic solution to the problem at hand is to introduce a more general soft cut: determinants can be partly compensation, partly responsibility. Still, in a first-best income tax framework, such a soft cut is possible only if the gross income function is additively separable. In case separability fits the data, a simple partial sharing rule emerges as a natural candidate for partial redistribution. This rule can be characterized on the basis of two simple properties, equal treatment of equals and partial solidarity. In case additive separability is rejected by the data, we propose two alternative solutions. Copyright Springer Science+Business Media New York 2015
