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

Author

Listed:
  • Edwin Fourrier-Nicolaï

    (UNITN - Università degli Studi di Trento = University of Trento)

  • Michel Lubrano

    (AMU - Aix Marseille Université)

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.

Suggested Citation

  • Edwin Fourrier-Nicolaï & Michel Lubrano, 2023. "Bayesian inference for non-anonymous growth incidence curves using Bernstein polynomials: an application to academic wage dynamics," Post-Print hal-04356211, HAL.
    • Handle: RePEc:hal:journl:hal-04356211
    DOI: 10.1515/snde-2022-0109
    Note: View the original document on HAL open archive server: https://hal.science/hal-04356211
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Conditional quantiles non-anonymous GIC Bayesian inference academic wage formation gender policy; Conditional quantiles; non-anonymous GIC; Bayesian inference; academic wage formation; gender policy;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions

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