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Quantile selection models: with an application to understanding changes in wage inequality

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  • Manuel Arellano

    (Institute for Fiscal Studies and Centre for Monetary and Financial Studies (CEMFI))

  • Stéphane Bonhomme

    (Institute for Fiscal Studies and University of Chicago)

Abstract

We propose a method to correct for sample selection in quantile regression models. Selection is modelled via the cumulative distribution function, or copula, of the percentile error in the outcome equation and the error in the participation decision. Copula parameters are estimated by minimizing a method-of-moments criterion. Given these parameter estimates, the percentile levels of the outcome are re-adjusted to correct for selection, and quantile parameters are estimated by minimizing a rotated “check” function. We apply the method to correct wage percentiles for selection into employment, using data for the UK for the period 1978-2000. We also extend the method to account for the presence of equilibrium e?ects when performing counterfactual exercises.

Suggested Citation

  • Manuel Arellano & Stéphane Bonhomme, 2015. "Quantile selection models: with an application to understanding changes in wage inequality," CeMMAP working papers CWP75/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:75/15
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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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