Bayesian inference for non-anonymous Growth Incidence Curves using Bernstein polynomials

Abstract

The paper examines the question of non-anonymous Growth Incidence Curves (na-GIC) from a Bayesian inferential point of view. Building on the notion of conditional quantiles of Barnett (1976), we show that removing the anonymity axiom leads to a non-parametric inference problem. From a Bayesian point of view,an approach using Bernstein polynomials provides a simple solution and immediate confidence intervals, tests and a way to compare two na-GICs. The paper illustrates the approach to the question of academic wage formation and tries to shed some light on whether academic recruitment leads to a super-star’s phenomenon, that is a large increase of top wages, or not. Equipped with Bayesian na-GIC's, our findings are that wages at Michigan State University experienced a top compression leading to a shrinking of the wage scale. Despite a pro-active policy, academic females are less paid that males. However, they get higher wage increase. But this is false for non-academics. Black and Asian manage to get higher wage increase than white, but Latin-Americans get much lower wage increase.