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

  • Edwin Fourrier-Nicolaï and Michel Lubrano ORCID logo EMAIL logo
Published/Copyright: July 24, 2023
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Studies in Nonlinear Dynamics & Econometrics
From the journal Studies in Nonlinear Dynamics & Econometrics Volume 28 Issue 2

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
JEL Classification: C11; C22; I23
Corresponding author: Michel Lubrano, Aix-Marseille Univ, CNRS, AMSE, 5 Bd Maurice Bourdet, 13001 Marseille, France, E-mail:

Funding source: Agence Nationale de la Recherche

Award Identifier / Grant number: ANR-17-EURE-0020

Acknowledgment

This paper was presented at the occasion of the 12th European Seminar on Bayesian Econometrics (ESOBE 2022) held in Salzburg, Austria in September 2022 in a session dedicated to Herman van Dijk to whom the Bayesian profession owes so much. Comments by the participants of ESOBE 2022, by Neil Shephard and by Herman van Dijk are gratefully acknowledged. During the writing of the first version this paper, we have benefited from very useful conversations with Mohammad Abu-Zaineh who provided a decisive help for identifying gender and ethnicity from names. The present version benefited from the remarks of two referees and of the editor. Discussions with Luc Bauwens are also gratefully acknowledged. Of course, remaining errors are solely ours.

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: The project leading to this publication has received funding from the French Government under the “France 2030” investment plan managed by the French National Research Agency (reference: ANR-17-EURE-0020) and from Excellence Initiative of Aix-Marseille University – A*MIDEX.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/snde-2022-0109).

Received: 2022-11-29
Accepted: 2023-07-07
Published Online: 2023-07-24

© 2023 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Editorial Introduction of the Special Issue of Studies in Nonlinear Dynamics and Econometrics in Honor of Herman van Dijk
  4. Review
  5. Challenges and Opportunities for Twenty First Century Bayesian Econometricians: A Personal View
  6. Research Articles
  7. Markov-Switching Models with Unknown Error Distributions: Identification and Inference Within the Bayesian Framework
  8. Dynamic Shrinkage Priors for Large Time-Varying Parameter Regressions Using Scalable Markov Chain Monte Carlo Methods
  9. Matrix autoregressive models: generalization and Bayesian estimation
  10. Sequential Monte Carlo with model tempering
  11. Modeling Corporate CDS Spreads Using Markov Switching Regressions
  12. Combining Large Numbers of Density Predictions with Bayesian Predictive Synthesis
  13. Bayesian inference for non-anonymous growth incidence curves using Bernstein polynomials: an application to academic wage dynamics
  14. Bayesian Reconciliation of Return Predictability
  15. A Dynamic Latent-Space Model for Asset Clustering
  16. Posterior Manifolds over Prior Parameter Regions: Beyond Pointwise Sensitivity Assessments for Posterior Statistics from MCMC Inference
  17. Bayesian Flexible Local Projections
https://doi.org/10.1515/snde-2022-0109
Keywords for this article
academic wage formation; Bayesian inference; conditional quantiles; gender policy; non-anonymous GIC

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Editorial Introduction of the Special Issue of Studies in Nonlinear Dynamics and Econometrics in Honor of Herman van Dijk
  4. Review
  5. Challenges and Opportunities for Twenty First Century Bayesian Econometricians: A Personal View
  6. Research Articles
  7. Markov-Switching Models with Unknown Error Distributions: Identification and Inference Within the Bayesian Framework
  8. Dynamic Shrinkage Priors for Large Time-Varying Parameter Regressions Using Scalable Markov Chain Monte Carlo Methods
  9. Matrix autoregressive models: generalization and Bayesian estimation
  10. Sequential Monte Carlo with model tempering
  11. Modeling Corporate CDS Spreads Using Markov Switching Regressions
  12. Combining Large Numbers of Density Predictions with Bayesian Predictive Synthesis
  13. Bayesian inference for non-anonymous growth incidence curves using Bernstein polynomials: an application to academic wage dynamics
  14. Bayesian Reconciliation of Return Predictability
  15. A Dynamic Latent-Space Model for Asset Clustering
  16. Posterior Manifolds over Prior Parameter Regions: Beyond Pointwise Sensitivity Assessments for Posterior Statistics from MCMC Inference
  17. Bayesian Flexible Local Projections
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