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

Edwin Fourrier-Nicolai () and Michel Lubrano
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Edwin Fourrier-Nicolai: School of International Studies and Department of Economics and Management, University of Trento, Italy.

No 2227, AMSE Working Papers from Aix-Marseille School of Economics, France

Abstract: 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 (search for similar items in EconPapers)
JEL-codes: C11 C22 I23 (search for similar items in EconPapers)
Pages: 30 pages
Date: 2022-12
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Related works:
Journal Article: Bayesian inference for non-anonymous growth incidence curves using Bernstein polynomials: an application to academic wage dynamics (2024)
Working Paper: Bayesian inference for non-anonymous growth incidence curves using Bernstein polynomials: an application to academic wage dynamics (2023)
Working Paper: Bayesian inference for non-anonymous growth incidence curves using Bernstein polynomials: an application to academic wage dynamics (2023)
Working Paper: Bayesian inference for non-anonymous Growth Incidence Curves using Bernstein polynomials: an application to academic wage dynamics (2022)
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