Michel Lubrano

This paper examines the question of non-anonymous Growth Incidence Curves from a Bayesian inferential point of view. Building on the notion of conditional quantiles, it shows that removing the anonymity axiom leads to a non-parametric inference problem. From a Bayesian point of view, an approach using Berstein polynomials provides a simple solution and immediate confidence intervals and tests. The paper illustrates the approach to the question of academic wage formation and tries to shed some light on the question to know if academic recruitment leads to a super stars phenomenon. Equipped with Bayesian na-GIC, we show that wages at Michigan State University experienced a compression leading to a shrinking of the wage scale, even female professors seem to be better treated than males.