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Location-Scale and Compensated Effects in Unconditional Quantile Regressions

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  • Martinez-Iriarte, Julian
  • Montes-Rojas, Gabriel
  • Sun, Yixiao

Abstract

This paper proposes an extension of the unconditional quantile regression analysis to (i) location-scale shifts, and (ii) compensated shifts. The first case is intended to study a counterfactual policy analysis aimed at increasing not only the mean or location of a covariate but also its dispersion or scale. The compensated shift refers to a situation where a shift in a covariate is compensated at a certain rate by another covariate. Not accounting for these possible scale or compensated effects will result in an incorrect assessment of the potential policy effects on the quantiles of an outcome variable. More general interventions and compensated shifts are also considered. The unconditional policy parameters are estimated with simple semiparametric estimators, for which asymptotic properties are studied. Monte Carlo simulations are implemented to study their finite sample performances, and the proposed approach is applied to a Mincer equation to study the effects of a location scale shift in education on the unconditional quantiles of wages.

Suggested Citation

  • Martinez-Iriarte, Julian & Montes-Rojas, Gabriel & Sun, Yixiao, 2022. "Location-Scale and Compensated Effects in Unconditional Quantile Regressions," University of California at San Diego, Economics Working Paper Series qt89z1w74z, Department of Economics, UC San Diego.
    • Handle: RePEc:cdl:ucsdec:qt89z1w74z
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    References listed on IDEAS

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    1. Fortin, Nicole & Lemieux, Thomas & Firpo, Sergio, 2011. "Decomposition Methods in Economics," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 4, chapter 1, pages 1-102, Elsevier.
    2. Eric A. Hanushek & Ludger Woessmann, 2008. "The Role of Cognitive Skills in Economic Development," Journal of Economic Literature, American Economic Association, vol. 46(3), pages 607-668, September.
    3. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    4. Melly, Blaise, 2005. "Decomposition of differences in distribution using quantile regression," Labour Economics, Elsevier, vol. 12(4), pages 577-590, August.
    5. Atsushi Inoue & Tong Li & Qi Xu, 2021. "Two Sample Unconditional Quantile Effect," Papers 2105.09445, arXiv.org.
    6. Pietro Emilio Spini, 2021. "Robustness, Heterogeneous Treatment Effects and Covariate Shifts," Papers 2112.09259, arXiv.org, revised Aug 2024.
    7. Sergio Firpo & Nicole M. Fortin & Thomas Lemieux, 2009. "Unconditional Quantile Regressions," Econometrica, Econometric Society, vol. 77(3), pages 953-973, May.
    8. David H. Autor & Lawrence F. Katz & Melissa S. Kearney, 2005. "Rising Wage Inequality: The Role of Composition and Prices," Harvard Institute of Economic Research Working Papers 2096, Harvard - Institute of Economic Research.
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    Cited by:

    1. Julian Martinez-Iriarte & YiXiao Sun, 2022. "Identification and Estimation of Unconditional Policy Effects of an Endogenous Binary Treatment: an Unconditional MTE Approach," Working Papers 131, Red Nacional de Investigadores en Economía (RedNIE).

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    More about this item

    Keywords

    Social and Behavioral Sciences; Quantile regression; unconditional policy effect; unconditional regression;
    All these keywords.

    JEL classification:

    • J01 - Labor and Demographic Economics - - General - - - Labor Economics: General
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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