Bayesian inference for non-anonymous growth incidence curves using Bernstein polynomials: an application to academic wage dynamics

Edwin Fourrier-Nicolaï and Michel Lubrano

Studies in Nonlinear Dynamics & Econometrics, 2024, vol. 28, issue 2, 319-336

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. “The Ordering of Multivariate Data.” Journal of the Royal Statistical Society: Series A 139: 318–55), we show that removing the anonymity axiom leads to a complex and shaky curve that has to be smoothed, using a non-parametric approach. We opted for a Bayesian approach using Bernstein polynomials which provides confidence intervals, tests and a simple way to compare two na-GICs. The methodology is applied to examine wage dynamics in a US university with a particular attention devoted to unbundling and anti-discrimination policies. Our findings are the detection of wage scale compression for higher quantiles for all academics and an apparent pro-female wage increase compared to males. But this pro-female policy works only for academics and not for the para-academics categories created by the unbundling policy.

Keywords: academic wage formation; Bayesian inference; conditional quantiles; gender policy; non-anonymous GIC (search for similar items in EconPapers)
JEL-codes: C11 C22 I23 (search for similar items in EconPapers)
Date: 2024
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Related works:
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)
Working Paper: Bayesian inference for non-anonymous Growth Incidence Curves using Bernstein polynomials: an application to academic wage dynamics (2022)
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DOI: 10.1515/snde-2022-0109

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