Bayesian inference for non-anonymous Growth Incidence Curves using Bernstein polynomials: an application to academic wage dynamics

Edwin Fourrier-Nicolai (1) , Michel Lubrano (2)

1 University of Trento [Trento]
2 AMSE - Aix-Marseille Sciences Economiques

This 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-GIC. The paper illustrates the approach to the question of academic wage formation and tries to shed some light on wether academic recruitment leads to a super stars phenomenon, that is a large increase of top wages, or not. Equipped with Bayesian na-GIC's, we show that wages at Michigan State University experienced a top compression leading to a shrinking of the wage scale. We finally analyse gender and ethnic questions in order to detect if the implemented pro-active policies were efficient.

Keywords: Conditional quantiles; non-anonymous GIC; Bayesian inference; wage formation; gender policy; ethnic discrimination

Domains: Humanities and Social Sciences; Economics and Finance

Edwin Fourrier-Nicolai, Michel Lubrano. Bayesian inference for non-anonymous Growth Incidence Curves using Bernstein polynomials: an application to academic wage dynamics. 2022. ⟨hal-03880243⟩