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Bayesian Unconditional Quantile Regression: An Analysis of Recent Expansions in Wage Structure and Earnings Inequality in the U.S. 1992-2009

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  • Michel Lubrano

    (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

  • Abdoul Aziz Junior Ndoye

    (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

Abstract

We develop a reliable Bayesian inference for the RIF-regression model of Firpo, Fortin and Lemieux (Econometrica, 2009) in which we first estimate the log wage distribution by a mixture of normal densities. This approach is pursued so as to provide better estimates in the upper tail of the wage distribution as well as valid confidence intervals for the Oaxaca-Blinder decomposition. We apply our method to a Mincer equation for analysing the recent changes in the U.S. wage structure and in earnings inequality. Our analysis uses data from the CPS Outgoing Rotation Group (ORG) from 1992 to 2009. We find first that the largest part (around 77% on average) of the recent changes in the U.S. wage inequality is explained by the wage structure effect and second that the earnings inequality is rising more at the top end of the wage distribution, even in the most recent years. The decline in the unionisation rate has a small impact on total wage inequality while differences in returns to education and gender discrimination are the dominant factors accounting for these recent changes.

Suggested Citation

  • Michel Lubrano & Abdoul Aziz Junior Ndoye, 2012. "Bayesian Unconditional Quantile Regression: An Analysis of Recent Expansions in Wage Structure and Earnings Inequality in the U.S. 1992-2009," Working Papers halshs-00790688, HAL.
    • Handle: RePEc:hal:wpaper:halshs-00790688
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00790688
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    References listed on IDEAS

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    Cited by:

    1. El Moctar Laghlal & Abdoul Aziz Junior Ndoye, 2018. "A Hybrid MCMC Sampler for Unconditional Quantile Based on Influence Function," Econometrics, MDPI, vol. 6(2), pages 1-11, May.
    2. Majda Benzidia & Michel Lubrano, 2016. "A Bayesian Look at American Academic Wages: The Case of Michigan State University," AMSE Working Papers 1628, Aix-Marseille School of Economics, France.
    3. Hajime Seya & Kay W. Axhausen & Makoto Chikaraishi, 2020. "Spatial unconditional quantile regression: application to Japanese parking price data," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 65(2), pages 351-402, October.

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    More about this item

    Keywords

    unconditional quantile; influence function; Oaxaca-Blinder decomposition; Bayesian inference; quantile regression;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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